Comparison and Analysis of Various Buffer Cache Management Strategies for Database Management System

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1 Comparison and Analysis of Various Buffer Cache Management Strategies for Database Management System Priti M Tailor 1, Prof. Rustom D. Morena 2 1 Assistant Professor, Sutex Bank College of Computer App. & Science, Amroli, Surat, Gujarat, India 2 Professor, Department of Computer Science, Veer Narmad South Gujarat University, Surat, Gujarat, India 1 first.author@xyx.com, 2second.author@xyx.com Abstract: The focus of this paper is to compare selected database buffer cache management strategy for databases. It includes comparative study among database buffer cache management strategies used by various databases like LRU, LFU, Modified LRU, and touch count algorithm based on the simulation results. Keywords: Database Buffer Cache Management, Page Replacement policies, LRU, LFU, Touch Count. 155 I. INTRODUCTION Mass data is stored on disks; Data can only be manipulated in the main memory of the computer and fetching objects from hard disk is costlier compared to RAM [5]. Therefore, part of the database has to be loaded into the main memory for processing and written back to disk after processing. Database buffer cache is a place in main memory used for caching the data and index [8]. The purpose of database buffer cache is to reduce disk IO by keeping frequently used object memory resident [5]. In their Five Minute Rule, Gray and Putzolu stated We are willing to pay more for memory buffers up to a certain point, in order to reduce the cost of disk arms for a system [1]. Database Buffer Cache Management is Key to provide efficient access to data and optimal use of main memory. The problem of buffer management in database management systems is concerned with the efficient main memory allocation and management for answering database queries [11]. A major factor for increasing overall performance is improving the database buffer cache management [5]. Read latency can be reduced and distinct write operations can be accumulated using database buffer cache. Database Buffer Cache reduces physical reads and writes, thus assists to overcome speed gap between processor and storage devices. Good use of the buffer can significantly improve the throughput and response time of any data intensive system [2]. The critical buffering decision arises when a new buffer slot is needed for a page about to be read in from disk, and all current buffers are in use. At this juncture the question that arises is which current page should be dropped from buffer. This is known as the page replacement policy and the different buffering algorithms take their names from the type of replacement policy they impose [1]. Cache replacement policies can be categorized broadly into three categories as specified by [4]. Recency-Based Policies: This type of policies evicts pages based on the recency-time of last reference of object i.e. reference time. Cache replacement algorithms in traditional memory systems deal with uniform cost/size objects. LRU is the most widely used cache replacement algorithm. Frequency Based Policies: This type of policies evicts pages based on frequency-number of times the object has been referenced. The basic frequency-aware replacement algorithm is LFU. Frequency based strategies use a property of request streams known as spatial locality, the likelihood that an object will appear again based on how often it s been seen before [12]. Recency/Frequency Based Policies: This type of policies evicts pages based on both recency and frequency. They may have more implementation overhead. If this type of strategy is designed properly then the problem of recency and frequency can be removed. Example of this type of strategy is LRU with touch count algorithm. A. LRU: LRU has been applied successfully in many different areas [1]. Gemstone and Versant uses LRU for replacing object in object buffer. Oracle also used standard LRU for database buffer cache replacement which is recency based algorithm; Most of recency based algorithms are more or less ex- tensions of the well-known LRU strategy. It is simple to implement and fast. Any time buffer was touched or brought into the cache, it was promoted to the head of the LRU list. Replacement takes place at the tail of the list. In LRU When a new object is needed, the object in the buffer that has not been accessed for the longest time is replaced i.e. the object at the tail of list is removed [5]. So, insertion and replacement of object is simple and have very low overhead. In this searching can be supported by various hashing techniques. In LRU cache can be polluted by arbitrary bursts of accesses to an infrequently accessed data set. For example large index scan or full table scan will fill the cache completely and remove the entire popular buffer [5]. After usage of LRU, oracle shifted to Modified LRU. Blocks brought into the cache from a single block read are placed at the head of the LRU. Blocks brought into the cache from a multi block read are placed near the

2 end of the LRU (the LRU end of the LRU). The good: a full table scan will not replace all cached buffers. The bad: A large index range scan (which can read many B*-Tree leaf blocks) can be single block reads, which can replace all the popular buffers. LRU is a simple algorithm with low overhead. LRU is likely to perform better in case of batched references i.e. a particular object is referred many times over a short period of time and then not at all. LRU is also better for random references or for the references in which some objects are referred more than others. In modified LRU full table scan will not replace all cached buffers but a large index range scan (which can read many B*-Tree leaf blocks) can be single block reads, which can replace all the popular buffers [5]. B. LRU with Mid-Point Insertion: This algorithm is similar to LRU strategy but the difference is Mid-Point insertion. One Mid Pointer is maintained to partition the list into Hot and Cold. In this type of strategy the list is divided into two parts hot portion and cold portion. Elements in the list before mid-point are considered in hot portion and elements starting from Mid-Point come within cold portion. Instead of inserting the new element at the head of the LRU list as in simple LRU, new element is inserted at the end of hot portion of the list. The element will be promoted to the hot end if it is referred frequently. If the element is not referred for long period of time again then the element is demoted to the cold end. Whenever a new element is referred which is not in the LRU list, and there is no free place in the buffer, an element from the end of the cold end is removed to make the space for new element [5]. Because of Mid-Point insertion the replacement of hot popular buffer is avoided. Here buffer in the hot list will not be replaced due to a large index range search [5]. C. LFU: LFU, MFU etc. are the example of frequency based algorithm. Frequency based page replacement algorithms uses reference count. Whenever a page is referred its reference count is incremented. The object will be replaced based on value of reference count. LFU replaces the page with minimum reference count [5]. LFU works well if small number of objects is referred frequently out of a large number of items. LFU suffers from the problem of counter overflow, certain pages building up high reference counts and never being replaced even though it will not be used again for a decent amount of time. This leaves other blocks which may actually be used more frequently to be replaced. LFU can replace new pages just entered into cache which have lower reference count which are going to be referred in near future. To protect cache from this pollution, aging can be used. For aging, reference counter is reset after it reaches to a predefined thresh hold limit i.e. maximum frequency value [5]. D. LRU with Touch Count: As indicated in [3] after modified LRU, LRU with touch count evolved which is the good mixture of recency and frequency based algorithms. The immense change in this algorithm was mid-point insertion. This change alone stopped the entire cache (actually a single LRU) from being substituted in almost any situation. The LRU list is divided into two parts hot region and cold region separated by a mid-pointer. The new buffer is added at mid-point. Each buffer has a touch count associated with it to indicate its popularity. Touch count is incremented when the buffer is touched after a specified time limit (3 seconds by default) [5]. When there is a need for buffer replacement then search is started from cold region, if the touch count of buffer is greater than _db_aging_hot_criteria(by default 2) then block is moved to hotregion and its touch count is reset to _db_aging_stay_count(by default 0). In this process some buffers may move from hot region to cold region in order to maintain hot and cold region ratio. If the buffer is moved from hot to cold region then its touch count is set to _db_aging_cool_count (1 by default). Because of all these criteria it is very difficult for a buffer to remain in hot region of LRU list [3]. Most computer systems use a global page replacement policy based on the LRU principle to approximately select a Least Recently Used page for a replacement in the entire user memory space [10]. II. LITERATURE SURVEY Chirag A. Shallahamer introduced touch count based data buffer management algorithm to address the growing size, performance requirements, and complexities of relational database management systems [3]. This algorithm reduced latch contention. This paper details oracle s touch count algorithm, how to monitor its performance, and how to manage for optimal performance. Two Main parts of this algorithm is midpoint insertion and touch count incrementing [13]. Stefan Podlipnig and Laszlo Boszormenyi have given an exhaustive survey of cache replacement strategies proposed for Web caches in. They concentrated on proposals for proxy caches that manage the cache replacement process at one specific proxy. A simple classification scheme for these replacement strategies was given and used for the description and general critique of the described replacement strategies. Although cache replacement is considered as a solved problem, they showed that there are still numerous areas for interesting research [6]. 156

3 In [7] authors discussed Least Recently / Frequently Used page replacement policy subsuming both the LRU and LFU policies. The LRU policy does block replacement by attaining the recency of block references while the LFU policy considers the frequency of block references. These respective policies are inherently assuming that future behaviour of the workload will be dominated by the recency or frequency factors of past behaviour. The LRFU policy associates a value with each block. This value is called the CRF (Combined Recency and Frequency) value and quantifies the likelihood that the block will be referenced in the near future. In [8] authors discuss spectrum of possible strategies for searching the buffer. Hash techniques on buffer information tables with overflow chaining are recommended as the most efficient implementation alternative for the buffer search function. Authors had shown the optimization potential of some of the new algorithms. Since they are parameterized, they can be tailored to a specific DBMS and application environment. The basic trade-off is the conceptual simplicity of the old algorithms versus a potential improvement in performance with the new algorithms. In [9] authors discussed various replacement strategies to consider when designing web server. Authors discussed comprehensive study of recency, frequency, and recency/frequency based strategies. Authors have provided several explanations of the results detailing various performance issues of the strategies individually and compared to other strategies. Authors also demonstrated that commonly used methods are generally outperformed by their derivative strategies. By combining web object characteristics together, the cache replacement strategies chose better victims in their decision process. III. Page Faults with 20% Repeated Pages EXPERIMENTAL ANALYSIS AND RESULTS This experimental analysis is done to compare various page replacement algorithms so, that better algorithm can be used in designing database buffer cache management. To compare various page replacement policies simulators have been developed using C language for page replacement policies like LRU, LRU with mid-point insertion, LRU Mid-Point insertion with touch count, and LFU. To test all the four algorithms on same environment one page reference string generator is developed to generate page reference string of specified criteria. Page reference string is divided into four groups based on the number of repeated pages i.e. group of 20% repeated pages, 30% repeated pages, 35% repeated pages and 40% repeated pages. 20% repeated pages mean out of 100, 20 pages are referenced again i.e. referenced second time. Page reference strings were generated using random number generator to get closer to the actual environment. Three different page reference strings were generated for this experiment for each group for pages 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500 and pages, each time using a different seed for the random number generator. The result of the conducted experiment is displayed in table and chart form. Result table displays average number of page-faults for all the three generated reference strings for 25%, 30%, 35%, and 40% cache sizes. 157

4 Page Faults with 30% Repeated Pages Page Faults with 35% Repeated Pages 158

5 Page Faults with 40% Repeated Pages 159

6 IV. CONCLUSION LRU Mid-point insertion with touch count is a very good combination of recency and frequency based policies. It performs better than LFU, LRU, and LRU with mid-point insertion. It is simple to implement and also does not create too much overhead. Optimal option to implement page replacement policy for database buffer cache management is LRU mid-point insertion with touch-count. V. REFERENCES [1] Elizabeth J. O Neil, Patrick E. O Neil, Gerhard Weikum, The LRU-K Page Replacement Algorithm For Database Disk Buffering, In Proc. Of the 1993 ACM SIGMOD international conference on Management of data, Washington D.C., USA, August 1993, pp [2] Theodore Johnson, Dennis Shasha, 2Q: A Low Overhead High Performance Buffer Management Replacement Algorithm, Proceedings of the 20th VLDB Conference Santiago, Chile, 1994, ISBN: , pp [3] Chirag A. Shallahamer, All about Oracle s Touch Count Data Block Buffer Cache Algorithm, Version 4a, January 5, OraPub, [4] Jin, Shudong, Bestavros, Azer, GreedyDual* Web Caching Algorithm: Exploiting the Two Sources of Temporal Locality in Web Request Streams, Computer Communications, Volume 24 Issue 2, February, 2001,pp [5] Priti Tailor, Dr. R. D. Morena, A Survey of Database Buffer Cache Management Approaches, International Journal of Advanced Research in Computer Science,Volume 8, No. 3,March April 2017, ISSN NO: [6] Stefan Podlipnig, Laszlo Boszormenyi, A Survey of Web Cache Replacement Strategies, ACM Computing Surveys, Vol. 35, No. 4, December 2003, pp [7] Donghee Lee, Jongmoo Choi, Jong Hun Kim, Sam H. Noh, Sang Lyul Min, yookun Cho, Chong Sang Kim, On the Existence of a Spectrum of Policies that Subsumes theleast Recently Used (LRU) and Least Frequently Used (LFU) Policies, Newsletter, ACM SIGMETRICS Performance Evaluation Review, Vol 27, Issue 1, June 1999, New York, NY, USA, pp ,. [8] W. Effelsberg, T.Haerder, Principles of Database Buffer Management,ACM Transactions on Database Systems, Vol 9, No 4, December 1984, pp [9] S.Ramano, H.ElAarag, A Quantative Study of Recency and Frequency Based Web Cache Replacement Strategies, CNS, 2008, Pages 70-78,ISBN: [10] Song Jiang, Xiaodong Zhang, Token-ordered LRU: an effective page replacement policy and its implementation in Linux systems,performance Evaluation, Vol 60, Issues 1 4, May 2005, pp [11] C. Faloutsos, R. Ng, and T. Sellis, Flexible and Adaptable Buffer Management Techniques for Database Management Systems, IEEE Transactions on Computers, vol. 44, no. 4, 1995, pp [12] B. Davison, A Web Caching Primer, IEEE Internet Computing, vol. 5, no. 4, pp , [13] Optimal Buffer Management Strategy for Object Oriented Databases, vol. 4, no. 1, pp , Jul